Architect - Platform Engineering

Quantiphi; Chicago (IL); On-site; USD 180,000 - 240,000; Full time

Job summary

Quantiphi is seeking a Senior MLOps Architect in Chicago to lead the architecture and delivery of large-scale ML/LLM pipelines on Google Cloud. You will guide cross-functional teams, define standards for monitoring, governance, and secure deployments across enterprise environments.

The role requires deep experience with Vertex AI, GKE and ML lifecycle management, plus mentoring of engineers on modern MLOps practices. Chicago-based, full-time role with cutting-edge AI initiatives.

Qualifications

• Deep proficiency with GCP native AI/ML stack and Vertex AI platforms.
• Hands-on ML lifecycle management from data to deployment.
• Experience building and operating MLOps/LLMOps pipelines.

Responsibilities

• Architect and implement the MLOps strategy aligned with proposals and roadmaps.
• Design enterprise ML/LLM pipelines including training, validation, deployment and CI/CD.
• Build container-oriented ML platforms (GKE-first) and evaluate orchestration tools.
• Implement hybrid MLOps + LLMOps workflows with governance and monitoring.
• Serve as technical authority across internal and client projects on GCP.
• Enable observability, drift detection and auditability in ML/LLM systems.
• Collaborate with data engineering, platform, DevOps and stakeholders to productionize solutions.
• Ensure security, governance and compliance for GCP services and workloads.
• Conduct architecture reviews and guide teams through implementations.
• Mentor engineers on MLOps tools and Vertex AI capabilities.

Skills

GCP AI/ML stack

Python

Cloud-native development

MLOps

Tools

Vertex AI

GKE

Cloud Functions

AutoML

Vertex AI Pipelines

BigQuery ML

Kubernetes

CI/CD (Cloud Build/Deploy)

Kubeflow

Airflow

MLflow

Seldon

OpenAI models

Anthropic models

Llama models

Prometheus/Grafana

Cloud Monitoring

Job description

Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies—we solve the problems that matter most to our clients.

Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries.

Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #SolvingWhatMatters.

We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including:

• 21 Google Cloud Partner of the Year awards in the past 10 years
• 3 AWS AI/ML Partner of the Year awards
• 3 NVIDIA Partner of the Year awards
• 3 Snowflake Partner of the Year awards
• Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms

Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators.

We are also proud to be certified as a Great Place to Work—reflecting our commitment to our people and our culture.

Experience Level: 10+ years

Employment type: Full Time

Location: Chicago, IL

Key Responsibilities:

• Architect and implement the MLOps strategy for the programme, ensuring alignment with the project proposal and delivery roadmap.
• Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation using GCP-native services.
• Build container-oriented ML platforms (GKE-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, Vertex AI, MLflow, Airflow, etc.).
• Implement hybrid MLOps + LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems within the GCP environment.
• Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks for GCP.
• Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems using tools like Cloud Monitoring and Vertex AI Model Monitoring.
• Collaborate with cross-functional teams — data engineering, platform, DevOps, and client stakeholders — to deliver production-ready ML solutions on Google Cloud.
• Ensure all solutions adhere to security, governance, and compliance expectations, particularly around handling GCP services, Google Kubernetes Engine workloads, and MLOps tools.
• Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms on GCP.
• Mentor engineers and provide guidance on modern MLOps tools, Vertex AI platform capabilities, and best practices.

Basic Qualification (BQ):

• Strong expertise in the Google Cloud (GCP) native AI/ML stack, including: Vertex AI (primary), Google Kubernetes Engine (GKE), Cloud Functions, AutoML, Vertex AI Pipelines, BigQuery ML, API Gateway, and CI/CD (Cloud Build/Cloud Deploy or equivalent).
• Hands-on experience with MLOps toolset and awareness of: MLflow, Kubeflow, Vertex AI Pipelines, Airflow, BentoML, KServe, Seldon.
• Deep understanding of model lifecycle management (feature engineering -> training -> registry -> deployment -> monitoring).
• Experience implementing or supporting LLMOps pipelines, including prompt versioning, evaluation metrics, and automation frameworks.
• Deep understanding of the ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
• Strong experience with Google Cloud's Vertex AI platform, including Pipelines, Feature Store, Model Registry, and Model Monitoring.
• Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
• Strong SQL and data transformation experience using Snowflake, Databricks, Spark.
• Experience with feature engineering pipelines and Feature Store management.
• Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, and reproducibility.
• Hands-on experience with Vertex AI Foundation Models, OpenAI, Anthropic, or Llama models.
• Experience with Cloud Monitoring, Vertex AI Model Monitoring, Prometheus/Grafana.
• Strong foundation in Python and cloud-native development patterns.
• Solid understanding of security best practices, Cloud IAM, secrets management, and artifact governance.

What’s in it for YOU at Quantiphi:

• Make an impact at one of the world’s fastest-growing AI-first digital engineering companies.
• Upskill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues.
• Work where innovation happens - work with disruptive innovators in a research-focused organization with 60+ patents filed across various disciplines.
• Stay ahead of the curve, immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies.